Opus-ClaimGuard - Auditable Hybrid Claim Vetting

Opus-ClaimGuard is a robust, end-to-end workflow designed specifically for the Applied AI Challenge to bring trust and efficiency back to insurance claims processing. As a solo developer, Anum Munir, I focused on demonstrating the highest level of platform efficiency and auditability achievable. The Solution: This project features a unique hybrid decision engine built on the Opus platform. It is capable of combining strict, rule-based policy checks (e.g., auto-rejecting claims with an expired license) with deep Generative AI analysis. Winning Feature: Multimodal Auditability. The system handles multimodal input by simultaneously analyzing the claim document (for extracted text data) and the damage photo (visual evidence). This allows the AI to perform a crucial consistency check and assign a Fraud Probability Score. For high-risk cases, the system generates a full Audit Log that includes the AI's Rationale (e.g., 'Minor damage does not match the $15,000 claim request'). This solves the 'black box' problem and provides complete $\text{Intake} \to \text{Deliver}$ traceability, which is essential for regulated industries. This project proves that one developer can build powerful, auditable, and enterprise-ready solutions.